Aim The inverse care law suggests that those with the greatest need for services are least likely to receive them. Our aim of this study was to test the inverse care law in relation to the use of health services by children aged 4–5 years in Australia who were developmentally vulnerable and socioeconomically disadvantaged.
Method Cross-sectional data were collected from the Longitudinal Study of Australian Children birth cohort when the children were aged 4–5 years. Children were grouped according to the combination of developmental vulnerability (yes, no) and socioeconomic disadvantage (lower, higher), resulting in four groups (reference group: developmentally vulnerable and disadvantaged). Multivariate regression was used to examine the impact of the combination of developmental vulnerability and disadvantage on health service use, adjusting for other sociodemographic characteristics.
Results 3967 (90%) of children had data on developmental vulnerability at 4–5 years. A third of children (32.6%) were classified as developmentally vulnerable, and 10%–25% of these children had used health services. Non-disadvantaged children who were developmentally vulnerable (middle need) had 1.4–2.0 times greater odds of using primary healthcare, specialist and hospital services; and non-disadvantaged children who were not developmentally vulnerable (lowest need) had 1.6–1.8 times greater odds of using primary healthcare services, compared with children who were developmentally vulnerable and disadvantaged (highest need).
Conclusion We found some evidence of the inverse care law. Equity in service delivery remains a challenge that is critically important to tackle in ensuring a healthy start for children.
- child health
- social inequalities
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Around one in five Australian preschool children have physical, socioemotional and learning difficulties that require additional support for school success—that is, they are ‘developmentally vulnerable’.1–4 Children who developmentally vulnerable are more likely to have poor educational outcomes in primary school and early high school.5 6 As adults, these inequities widen, presenting as higher levels of morbidity and mortality, lower levels of academic achievement, poorer employment opportunities and reduced participation in society.7–9
Children who are developmentally vulnerable have, by definition, additional health service needs including support for their parents/carers, specialist services for diagnostic assessment and therapy such as speech pathology.10 These services are critical in order to ameliorate, and in some cases prevent, adverse physical health, socioemotional and learning outcomes.9 Children who are developmentally vulnerable despite their higher healthcare needs are at greater risk of missing out on health services,10 11 with parents citing a number of barriers such as waiting times and cost of services across the healthcare system.1 4 12
The health service patterns of children who are developmentally vulnerable are likely to be shaped by other circumstances in their lives. Socioeconomic disadvantage is both a risk factor for developmental vulnerability and reduced use of health services.13 14 Children who experience socioeconomic disadvantage are 1.5–2 times more likely to be developmentally vulnerable than their more advantaged peers.2 6 13 15 Socioeconomic disadvantage is also associated with lower use of medical specialist services by children in Australia.16
The inverse care law was coined by Tudor Hart in the 1970s and states that ‘the availability of good medical or social care tends to vary inversely with the need of the population served’. It has been demonstrated in a range of populations and settings, such as for adult health services by geographic need.17 In the current setting, the inverse care law suggests that children who are developmentally vulnerable and socioeconomically disadvantaged (highest need) would have lower service use than those who are not developmentally vulnerable and not disadvantaged (lowest need). This aligns with the theoretical frameworks of cumulative risk and intersectionality of risk factors in relation to early childhood development.18–20 Yet, while health service patterns according to socioeconomic disadvantage and developmental vulnerability have been described separately,2 3 12 the combined effect on the use of health services by a preschool-aged child of being developmentally vulnerable and socioeconomic disadvantaged has not been examined.
Using data from the Longitudinal Study of Australian Children (LSAC), the aim of this study was to test the inverse care law in relation to the use of health services by children aged 4–5 years in Australia. Specifically, we tested whether disadvantaged children who were developmentally vulnerable (highest need) used less health services than non-disadvantaged children who were developmentally vulnerable or disadvantaged children who were not developmentally vulnerable (middle need) and non-disadvantaged children who were not developmentally vulnerable (lowest need). We hypothesised that children with the highest need would have less service use than those with lower need, consistent with an inverse care law.
Data were drawn from the LSAC B cohort. The LSAC is broadly representative of Australian children, except for those living in remote areas.21 The LSAC design, weighting and sampling methodology is well documented. In short, data were collected on children’s development as well as family and community characteristics. The methodology for data collection included a complex survey design with multiple information sources (including parent interview, direct child assessments and observational measures, parent and teacher self-report questionnaires, and linkage to administrative data sets).21 22
Developmental vulnerability was defined as children in LSAC at ages 4–5 years in Wave 3 who were in the bottom 15% of the LSAC Physical Outcomes Index (POI), Socioemotional Outcomes Index (SOI) and/or Learning Outcomes Index (LOI). These outcome indices, derived from validated tools,23 were developed and validated within LSAC for Waves 1–3, as a means of summarising progress within the three developmental domains of health and physical development, social and emotional functioning and learning competencies.23 Each index is a composite of direct measures of child and parent surveys and teacher-rated standardised assessments. The POI in Wave 3 is an overall rating of physical health, special healthcare needs, weight and quality of life. The SOI is an overall rating of internalising and externalising behaviour and social competence. The LOI is an overall rating of literacy, language and numeracy skills. The tools are well described in LSAC.23 Any child in the bottom 15% for any of these outcome indices was deemed ‘developmentally vulnerable’.
We examined the socioeconomic position (SEP) when the child was 4–5 years of age (Wave 3) by using the composite SEP variable previously developed in LSAC.24 In summary: parental income from all sources was summed and log transformed; parental education level was based on numbers of years of education from 0 to a maximum of 20 years. Parents’ occupations were based on current/most recent occupation based on a standardised tool developed by the Australian National University (ANU4) that groups occupations by skill and type from the Australian Standard Classification of Occupations.24 The individual measures were standardised (mean of 0, SD of 1), summed, divided by the number of parents in the home; this score was restandardised to produce a final continuous measure of SEP.24 Following previous LSAC analyses this continuous SEP score was dichotomised,14 25 with the lowest 20% coded as ‘disadvantaged’, and the other 80% of the sample coded as ‘not disadvantaged’.
Combination of developmental vulnerability and socioeconomic disadvantage
To explore the combined effect of developmental vulnerability and socioeconomic disadvantage, we further categorised children according to their socioeconomic status. We generated four categories reflecting both developmental vulnerability (yes, no) and socioeconomic disadvantage (yes, no). In keeping with our research questions, the children who were developmentally vulnerable and socioeconomically disadvantaged were defined as having ‘highest service need’, the children who were developmentally vulnerable and not disadvantaged/or the children who were not developmentally vulnerable and disadvantaged were defined as having ‘middle service need’, and the group who were not developmentally vulnerable and not disadvantaged were defined as having the ‘lowest service need’.
Health service use
Health service use at 4–5 years was measured by caregiver response to the question ‘In the last 12 months, have there been any of the services listed that the child has used? (yes/no)’. These included: primary healthcare services- maternal and child health nurse (MCHN) visits; general practitioners (GP); specialist services—speech therapy; paediatrician; other specialists; and hospital services—the emergency department (ED); hospital outpatient department (OPD); other medical services. These measures were also combined as a composite measure of ‘any health service use’.
Other covariates included as potential confounders were: the child’s sex, maternal country of birth and language spoken to the child other than English (LOTE) as defined by the Australian Bureau of Statistics26 27; and maternal relationship status (one vs two parents) at 4–5 years (Australian Institute of Family Studies-derived variable).28
Data analysis was in keeping with the recommendations for handling of LSAC survey data with weighting of Wave 3 for the multiwave longitudinal survey design, and likelihood of selection bias due to recruitment and non-response.29 Estimates of the prevalence of developmental vulnerability were calculated with corresponding 95% CIs. Univariate logistic regression was used to test for associations between developmental vulnerability in Wave 3 and child, parent, and family factors and health service use overall and for primary, specialist and hospital health services.
To examine the combination of socioeconomic disadvantage and developmental vulnerability, we used the combined variable with four categories: developmentally vulnerable and disadvantaged (highest need); developmentally vulnerable and not disadvantaged (middle need); not developmentally vulnerable and disadvantaged (middle need); not developmentally vulnerable and not disadvantaged (lowest need). Univariate logistic regression was used to test for associations between health service use and these categories (developmental vulnerability and socioeconomic disadvantage reference group). Multivariate regression was used to model (online supplementary table 2) the association between health service use and these groups (highest need reference category) with covariates of child’s sex, maternal country of birth, LOTE and maternal relationship status. All analyses were performed using Stata SE V.14 (StataCorp, College Station, TX).
There were a total of 4386 children aged 4–5 years in Wave 3. This represented 88% of the Wave 1 sample. Of the children aged 4–5 years in the sample, just over half were male and 8% had a chronic disability/medical condition that had lasted at least 12 months. Of the families in the sample, 35% of mothers were born in a country outside of Australia, 15% spoke a LOTE at home, 29% were headed by a single parent and there was an even distribution of the sample across the SEP quintiles. The analysis sample included the 3967 Wave 3 B cohort participants that had information on developmental vulnerability as defined by the composite variable. There were missing data on developmental vulnerability in 9.5% of the Wave 3 B cohort (table 1). Characteristics of the missing data are outlined in online supplementary table 1. Children with missing developmental vulnerability data were more likely to be born outside of Australia, speak a LOTE at home and be in the two lowest SEP quintiles.
Of the children with information on developmental vulnerability, a total of 1292 (32.6%) children were classified as ‘developmentally vulnerable’, 42% in the bottom 15% of the LOI, 39% in the bottom 15% of the SOI and 47% in the bottom 15% of the POI. Seventy-four per cent were developmentally vulnerable in one area only; 22% in two and 4% in all three. A higher proportion of children who were developmentally vulnerable at ages 4–5 years were boys, had a mother who was born outside of Australia, had a family that spoke a LOTE at home, came from single-parent families and were in the lowest SEP quintile (table 1).
Health service use in the last 12 months
Parents of 3403 (86%) children reported accessing a health service for their child in the last 12 months. In terms of primary healthcare services, this included 3119 (79%) who saw a GP and 538 (14%) who saw an MCHN. In terms of specialist services, 517 (13%) saw a speech pathologist, 324 (8%) saw a paediatrician and 190 (15%) saw an ‘other specialist’. A total of 749 (19%) went to the ED, 251 (6%) went to the OPD and 84 (6%) went to an ‘other medical’ service.
Health service use according to developmental vulnerability
The proportion of children who were developmentally vulnerable using health services overall (86.8% vs 85.3%) and GP services (78.9% vs 78.5%) reported in the last 12 months was similar to those who were not developmentally vulnerable. A smaller proportion of children who were developmentally vulnerable were reported to have seen an MCHN (11.1% vs 14.8%) when compared with children who were not developmentally vulnerable. A greater proportion of children who were developmentally vulnerable were reported to have seen a speech pathologist (19.3% vs 10.0%), a paediatrician (16.3% vs 4.3%), another specialist (14.7% vs 9.2%), an ED (24.9% vs 16%), OPD (10% vs 4.6%) and/or other medical service (6.5% vs 3.7%) when compared with children who were not developmentally vulnerable (figure 1).
Health service use according to socioeconomic disadvantage
Children who were disadvantaged were less likely to use any medical service, GP or MCHN compared with those who were not disadvantaged (figure 1).
Health service use according to the combination of developmental vulnerability and socioeconomic disadvantage
Examining the combination of developmental vulnerability and disadvantage through univariate and multivariate logistic regression analyses showed that within the two groups of children who were developmentally vulnerable with four exceptions (MCHN, speech pathology, OPD, other medical), non-disadvantaged children who were developmentally vulnerable (middle need) had 1.4–2.0 times greater odds of using all services (any, GP, paediatrician, other specialist, ED) than developmentally vulnerable children who were disadvantaged (highest need) (table 2).
Compared with children who were developmentally vulnerable and disadvantaged (highest need), children who were not developmentally vulnerable and disadvantaged (middle need) had 0.3–0.4 times less odds of using a speech pathologist and/or a paediatrician.
Compared with children who were developmentally vulnerable and disadvantaged (highest need), non-disadvantaged children who were not developmentally vulnerable (lowest need) had 1.4 times greater odds of using any health services, 1.6 –1.8 times greater odds of using a GP and/or a MCHN, 0.4-0.7 times less odds of using an OPD and/or the ED, and 0.4 times less odds of using a speech pathologist and/or a paediatrician.
For our hypothesis that the inverse care law exists for children aged 4–5 years in Australia, with those who have the highest need (developmentally vulnerable and socioeconomically disadvantaged) having less service use than other children (lower need) we had mixed findings. We have shown that children who were developmentally vulnerable and disadvantaged (highest need) reported more use of specialist services than children who were not developmentally vulnerable (middle and lowest need). However, the lowest need group (non-disadvantaged and not developmentally vulnerable) had between 1.6 and 1.8 greater odds of using primary healthcare services (GP/MCHN) than the highest need group (developmentally vulnerable and disadvantaged). In addition, children who were developmentally vulnerable and not disadvantaged (middle need) had between 1.4 and 2 times greater odds of using primary healthcare (a GP), specialist services (paediatrician/other specialist) and/or hospital services (ED) than the highest need group (developmentally vulnerable and disadvantaged).
In Australia at a federal level, there is a universal health coverage system where GPs, specialists and public hospital services for children are free of charge using Medicare to the user. There is also a private healthcare system with out-of-pocket expenses.30 Given that children who are developmentally vulnerable have physical, socioemotional and learning needs, one would expect this group of children to have greater use of GPs and specialist services compared with children who are not developmentally vulnerable. However, we found no difference in the use of GP services when this variable was examined in isolation despite the increased needs present. Perhaps this is not surprising given the Australian Immunisation Schedule31 has fully funded 4-year-old immunisations with the bulk of immunisation done by GPs. Another possible explanation lies in the fact that parents of children who have special needs report only using GPs for what they perceive to be simple problems.32 33 As one would expect, there was less use of specialist services such as paediatricians and speech pathologists in children who were not developmentally vulnerable.
A more complex picture emerged when we further examined the combination with socioeconomic disadvantage where we found that those children who were not developmentally vulnerable and not disadvantaged (lowest need) and those children who were developmentally vulnerable and not disadvantaged (middle need) had greater odds of using a GP than their developmentally vulnerable and disadvantaged counterparts (highest need). One could argue that although the majority of Australian GPs use Medicare,34 socioeconomic disadvantage remains a barrier to a child using a GP due to other out-of-pocket expenses, cultural barriers and parental health literacy as reported by some service providers and parents.12 35 This may be compounded in the context of developmental vulnerability, where families face a range or additional challenges and stressors and where financial strains may be further stretched because of the higher costs associated with higher care needs.
Those who were developmentally vulnerable and not disadvantaged (middle need) also had greater odds of using a paediatrician, and other specialists compared with those who were developmentally vulnerable and disadvantaged (highest need)—this is consistent with the inverse care law. This is a concerning finding given the key role that paediatricians play in diagnostic evaluation, assessment of underlying causes and comorbidity, and funding support (including Carer’s Allowance, and the National Disability Insurance Scheme for children who are developmentally vulnerable).36 This finding of reduced use of specialists in Australia is consistent with Medicare data for older children in LSAC who are disadvantaged.16 It has been well documented that there are long waiting lists to see publicly funded paediatricians, and wide variation in billing and out-of-pocket expenses for private paediatricians.37
Limitations and future directions
This paper relies on reported use of health services where there may be issues in terms of understanding and recall of what services were attended depending on parent’s/carer’s needs and level of disadvantage. Another limitation is that the children more likely to have missing data include those who are socioeconomically disadvantaged.29 One would expect this attrition to result in an underestimation of the impact of socioeconomic disadvantage, thus making our findings even starker. Although the Wave 3 B cohort aged 4–5 years may no longer be representative of Australian children currently aged 4–5 years, it is the most up-to-date and complete data set of Australian children available in terms of their developmental vulnerability and disadvantage.
We have shown evidence of the inverse care law that should be examined in further in future research. For example, advances in causal mediation analysis will provide an opportunity to disentangle processes along this pathway, and effect measure modification can help to further quantify the combined impact of developmental vulnerability and disadvantage.38 39 Investigating how developmental vulnerability and SEP intersect with other sources of marginalisation and social stratification is also important; for example, Indigenous status given continuing high levels of discrimination and structural and interpersonal racism faced by Indigenous Australians, and this would be most appropriately examined using the Longitudinal Study of Indigenous Children cohort.
Equity in use of healthcare is not about an equal distribution of health service use across groups of children, regardless of their level of developmental vulnerability. For true equity, there needs to be enhanced services (more intense, delivered differently or financial barriers removed) for children in the highest need group—being those children who have the combined risk factors for long-term adverse outcomes. To address the inverse care law in Australia we need to develop and evaluate innovative models of care that address the service needs of all children.
What is already known on this subject
Preschool-aged children who are socioeconomically disadvantaged are more likely to be developmentally vulnerable than their more advantaged peers.
Children who are developmentally vulnerable have greater need for health services.
Socioeconomic disadvantage is associated with reduced use of health services.
What this study adds
Preschool-aged children who are developmentally vulnerable and socioeconomically disadvantaged (ghighest health needs) are less likely to use health services than children who are developmentally vulnerable and not disadvantaged. This is an example of the ‘inverse care law’.
Health services need to be designed equitably, to facilitate access and availability for this high need group of children.
We thank the LSAC study participants, without them this study would not have been possible.
Twitter Sue Woolfenden @WoolfendenSusan
Contributors SW planned, conducted (including acquisition and analysis of data) and reported the work described in the article and was responsible for the overall content as guarantor. CG, MOC, HB, SG and AMK contributed to study planning, data analysis plan and interpretation of results. RL, KW, DR, NB and HSS reviewed and gave content, editorial and methodological advice on drafts of the initial manuscript. All authors (SW, CG, HB, HSS, RL, NB, DR, KW, AMK, MOC) were involved in revising the manuscript critically for important intellectual content and have given final approval of the version to be published.
Funding SW is supported by the National Health and Medical Research Council Career Development Fellowship (1158954) and the Research Foundation of Cerebral Palsy Alliance. HSS is supported by the National Health and Medical Research Council Early Career Fellowship (1144566) and the Australasian Cerebral Palsy Clinical Trials Network. SG is supported by the National Health and Medical Research Council of Australia Practitioner Fellowship (1155290). Research at the MCRI is supported by the Victorian Government’s Operational Infrastructure Support Program. HB is supported by an RMIT University Vice Chancellor’s Senior Research Fellowship.
Competing interests None declared.
Patient consent for publication Not required.
Ethics approval Ethical approval was granted to conduct this study by the Human Research Ethics Committee, Royal Children’s Hospital (approval number 24051).
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data may be obtained from a third party and are not publicly available. All data used in this manuscript are available from the Longitudinal Study of Australian Children (https://growingupinaustralia.gov.au/).